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研究生： WILLIAM ONNYXIFORUS PURNOMO
WILLIAM ONNYXIFORUS PURNOMO
論文名稱： USING GENETIC ALGORITHM AND HEURISTIC APPROACH FOR PLATELET MANAGEMENT OF BLOOD CENTER TO REDUCE SHORTAGE AND WASTAGE
USING GENETIC ALGORITHM AND HEURISTIC APPROACH FOR PLATELET MANAGEMENT OF BLOOD CENTER TO REDUCE SHORTAGE AND WASTAGE
指導教授： 林希偉
Shi-Woei Lin
口試委員: 林希偉
Shi-Woei Lin
Ming-Che Hu
Ming-Che Hu
王孔政
Kung-Jeng Wang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 52
中文關鍵詞： 分離術血小板 、庫存管理 、遺傳算法 、啟發式規則
外文關鍵詞： apheresis platelet, inventory management, genetic algorithm, heuristic rules
相關次數： 點閱：170  下載：4 
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    • 由於血品的供應與需求存在相當大的不確定性和變異性，減少血液製品的短缺和浪費一直是血庫和醫院在血品產出和庫存管理上的主要挑戰。本研究旨在開發當單靠分離術血小板的採集量不足以支應醫療所需時，可用於分離術血小板收集和血小板濃厚液（即隨機捐血者血小板）產出的優化模型。在本研究中，我們使用捐血中心每日採集之分離血小板單位數量以及提供醫院的血小板單位數量之時間序列資料，並使用遺傳演算法（genetic algorithm, GA）和模擬方法進行模型的建構與求解。其中GA旨在求算分離血小板的每日最適目標採集量，接著再透過制定模擬模型比較幾個血小板濃厚液的生產規則來降低短缺、逾期和庫存之總成本。本研究之GA模型得出周一至週日的分離血小板採血目標，並透過模擬模型驗證了所提出的血小板濃厚液之合適生產規則，此最佳目標收集量和生產規則之組合可以幫助捐血中心滿足醫院的需求。本研究中提出的方法可以提高血液收集、生產和供應的管理效率，並減少逾期浪費和血液短缺的成本。

    Minimizing shortages and wastage of blood products is a major challenge of production and inventory management faced by blood banks and hospitals. This issue deserves careful attention because there is considerable uncertainty and variability of demand. This research aims to develop an optimization model for Apheresis platelets collection and for platelet production when pooled whole-blood platelets (i.e., random platelets) are in need.
    Time series data of apheresis platelets collected from donors and platelets supplied to hospital daily in Taipei Blood Center from January 2013 to April 2014 used for model formulation. This model is then solved by using a combination of Genetic Algorithm (GA) and simulation approaches. In particular, GA is developed to solve the optimal daily target collection volumes that minimize the total cost of shortages, outdated, and inventory cost. A simulation model is then formulated to compare several inventory/production rules.
    The optimal daily target collection volumes for apheresis platelets with this following sequence 231, 242, 212, 198, 233, 259, 237 (from Monday to Sunday respectively) were obtained by using GA. The proposed policies for platelet production was also verified by using a simulation model to further improve the availability of platelet. The combination of optimal target collection volumes and proposed policy for random platelet production can help TBC to fulfill the demand from the hospital.
    The methods proposed in this study can enhance the management efficiency in blood collection, production, and supply of the blood center. TBC can be easily implemented in other blood centers to reduce the costs of wastages and shortages.

    COVER PAGE i MASTER’S THESIS RECOMMENDATION FORM ii QUALIFICATION FORM BY MASTER’S DEGREE EXAMINATION COMMITTEE iii 摘要 iv ABSTRACT v ACKNOWLEDGMENT vi TABLE OF CONTENTS vii LIST OF FIGURES x LIST OF TABLES xi CHAPTER 1 INTRODUCTION 1 1.1. Research Background 1 CHAPTER 2 LITERATURE REVIEW 3 2.1 Inventory Policy for Perishable Items 3 2.2 Inventory Management of Blood Products 4 2.3 Genetic Algorithm 6 2.4 Forecasting Method 7 2.5 Contribution to The Literature 9 CHAPTER 3 MATERIALS AND METHODS 10 3.1. Variables and Data 10 3.2. Problem Statement and Formulation 10 3.3. Research Procedures 12 CHAPTER 4 DETERMINE OPTIMAL TARGET COLLECTION AMOUNT FOR APHERESIS PLATELET 14 4.1 Problem Definition 14 4.1.1 Mathematical Concept 14 4.1.2 Mathematical Formula 16 4.1.2.1 Notations 16 4.1.2.2 Assumptions 17 4.1.2.3 Mathematical Model 17 4.1.2.4 Objective Function 17 4.1.2.5 Constraints 18 4.2 The Proposed Genetic Algorithm 19 4.2.1 Chromosomes Representation 19 4.2.2 Population Generation 20 4.2.3 Fitness Value Calculation 20 4.2.4 Selection 20 4.2.5 Crossover Process 21 4.2.6 Mutation Process 22 4.2.7 Terminantion Process 23 4.3 Results of The Proposed GA 23 4.3.1 Sensitivity Ananlysis 23 4.3.2 Conclusions 26 CHAPTER 5 FINDING PRODUCTION POLICY 27 5.1 Problem Definition 27 5.2 Production Policies 27 5.2.1 Order-Up-To Level Policy 27 5.2.2 Modified Order-Up-To Level Policy 28 5.2.3 Weighted Mean-Variance Policy 28 5.2.4 Last Value Policy 29 5.2.5 Proposed Method 29 5.3 Prediction Methods 29 5.3.1. Linear Regression 30 5.3.2. Multilinear Regression 30 5.3.3. NN (Neural Network) Regression 30 5.3.4. Support Vector Regression (SVR) 30 5.3.5. ARIMA (Autoregressive Integrated Moving Average) 31 5.4 Result and Analysis 31 5.4.1. Result of The Prediction Method Comparison 31 5.4.2. Results from The Comparison of Heuristic Policies for The Deterministic Problem 32 5.4.3. Results from The Comparison of Heuristic Policies for The Stochastic Problem 32 5.5 Sensitivity Analysis 33 CHAPTER 6 CONCLUSIONS AND RECCOMENDATION 37 6.1 Conclusion 37 6.2 Managerial Implication 37 6.3 Limitation and Recommendation for Future Research 38 REFERENCES 39

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